1. In-silico PCR

  • Objectives and notes
    • Original coordinates define the location of Single Nucleotide Variants (SNV), centered in an amplicon
    • Run isPCR on GRCh38 genome to define amplicon START and END coordinates
    • Verify primer design specificity
    • Obtain amplicon sequence and calculate GC content
# Global R markdown code chunk options
knitr::opts_chunk$set(message=FALSE, 
                      warning = FALSE, 
                      error=FALSE, 
                      echo=TRUE, 
                      cache=TRUE, 
                      fig.width = 7, fig.height = 6, 
                      fig.align = 'left')

1.1 GRCh38 in-silico PCR summary

  • 1997 unique primers
  • 2086 isPCR results
  • 39 primer pairs with more than 1 predicted isPCR product.
  • 1958 primer pairs with only 1 isPCR product (Perfect_In_silico_specificity = TRUE)
  • 4 primer pairs without isPCR products
  • 2070 isPCR products with expected size range of 875:925, (Intended_interval = TRUE)
  • 2044 isPCR products within the expected chromosome (Intended_chr = TRUE)
  • 2040 isPCR products with expected product size and chromosome (Intended_amplicon = TRUE)
# Reading in silico PCR results

f_dir <- paste0(wd, "All_chrom_in_silico_PCR_files_GRCh38/")
f_list <- list.files(f_dir)

in_silico_list <- lapply(1:21, function(x) read.csv(paste0(f_dir, f_list[x]), header = TRUE))
df_in_silico <- rbindlist(in_silico_list)

#############

df_in_silico$X <- NULL

# Renaming columns to indicate their coordinate genome version 
colnames(df_in_silico)[10] <- "Chr_GRCh38_pred"
colnames(df_in_silico)[11] <- "Amplicon_Start_GRCh38"
colnames(df_in_silico)[12] <- "Amplicon_End_GRCh38"
colnames(df_in_silico)[13] <- "Amplicon_length_GRCh38"
colnames(df_in_silico)[15] <- "Sequence_GRCh38"

#head(df_in_silico[,1:14])
#dim(df_in_silico)  # 2086 PCR products from 1964 primer pairs.

# Flag amplicons with multiple PCR products
# In silico PCR was run with maxProductSize = 1500 bp.
# Most byproducts have much shorter lengths, making them likely to be packaged into viruses, which has a capacity of 900 bp. 
# In silico prediction of longer products that may not be packaged into AAV due to length constraint may cause reduced efficiency of PCR amplification and decreased abundance of these amplicons. 
# Conclusion: In-silico PCR confirmed high specificity of primer design, and viral packaging. 

# There are 39 amplicons with more than 1 predicted in silico PCR product.
df <- as.data.frame(table(df_in_silico$UNIQID))
unspecific_PCR <- dplyr::filter(df, Freq > 1)  

# Unspecific amplicons and their product sizes
#as.data.frame(dplyr::filter(df_in_silico, UNIQID %in% unspecific_PCR$Var1)[,c("UNIQID", "Amplicon_length_GRCh38", "PCR_efficiency")])

# A histogram of isPCR product lengths.
p1 <- ggplot(df_in_silico, aes(x = Amplicon_length_GRCh38))+
    geom_density()+
    geom_vline(xintercept = median(na.omit(df_in_silico$Amplicon_length_GRCh38)), color = "red", linetype = 2)+
    annotate("text", label = "median = 906 bp", x = 950, y = 0.05, size = 6, colour = "red")+
    theme_bw()+
    labs(title = "A histogram of all isPCR product lengths", x = "Amplicon lengths [bp]")+
    theme(plot.title = element_text(hjust = 0.5))+
    coord_cartesian(xlim = c(800, 1000))


# A histogram of isPCR product lengths predicted as unspecific
p2 <- ggplot(dplyr::filter(df_in_silico, UNIQID %in% unspecific_PCR$Var1), 
       aes(x = Amplicon_length_GRCh38))+
    geom_density()+
    theme_bw()+
    labs(title = "A histogram of unspecific isPCR product lengths", x = "Amplicon lengths [bp]")+
    theme(plot.title = element_text(hjust = 0.5))
plot_grid(p1, p2, 
          labels = c('A', 'B'), 
          rel_widths = c(1.2, 1.2),
          vjust = 1.5)
Histograms of isPCR products. (A) All isPCR products, n = 2086; (B) Unspecific isPCR products, n = 128; Unique primer pairs, n = 1997

Histograms of isPCR products. (A) All isPCR products, n = 2086; (B) Unspecific isPCR products, n = 128; Unique primer pairs, n = 1997 

# Conclusions: 
# 1. The majority of unspecific products have lengths compatible with AAV packaging
# 2. There are two PCR products of 1450 bp, and one 78 bp

# Defines Perfect_In_silico_specificity
df_in_silico$Perfect_In_silico_specificity <- ifelse(df_in_silico$UNIQID %in% as.character(unspecific_PCR$Var1), FALSE, TRUE)


# GC content calculation
GC_cont <- function(x){
  
x <- toupper(x)
num_g <- str_count(x, "G")
num_c <- str_count(x, "C")
gc_content <- (num_g + num_c) / str_length(x)
gc_content

}

df_in_silico$GC_content <- GC_cont(df_in_silico$Sequence)


# I'm flagging amplicons if they are intended products if they chromosomes match, and PCR product length is +/- 5 bp from the product predicted on hg19 genome. 

# Fixing X chrom name and chrom class encoding to permit logical operations
# The line below is an ugly hack.
#df_in_silico$CHROMOSOME <- ifelse(is.na(as.numeric(df_in_silico$CHROMOSOME)), 23, as.numeric(df_in_silico$chr))
#df_in_silico$Chr_GRCh38_pred <- as.numeric(df_in_silico$Chr_GRCh38_pred)
#df_in_silico$chr <- as.numeric(df_in_silico$chr)

# Removes 4 records lacking predicted PCR products.
d <- na.omit(df_in_silico)

#unique(setdiff(df_in_silico$UNIQID, d$UNIQID)) 
#No PCR prod for: 
# "1956_2_40385062_C_G_11869.p1", 
# "408_16_78441213_C_G_12937.p1", 
# "2712_3_68059361_A_C_13159.p1", 
# "940_17_35098691_G_A_14586.p1"

# This is different from STAR408, because we don't have product lengths in primer spreadsheet
# Unlike the STAR408 project, in silico PCR is the only source of amplicon sequence length.
# Start and End coordinates denounce SNVs, not amplicons.
# Amplicons were designed to be ~900 bases. 

# I called amplicons "Intended_interval" when they fall between 875 : 925.
d$Intended_interval <- sapply(1:nrow(d), function(x) d$Amplicon_length_GRCh38[x] 
       %in% c(875 : 925))

# Extracting extra amplicons without predicted in silico PCR products
no_PCR_amp <- dplyr::filter(df_in_silico, UNIQID %in% c("1956_2_40385062_C_G_11869.p1", "408_16_78441213_C_G_12937.p1", "2712_3_68059361_A_C_13159.p1", "940_17_35098691_G_A_14586.p1"))

no_PCR_amp$Intended_interval <- NA

# Adding amplicons lacking pred. PCR prod. to the full dataset
d2 <- rbind(d, no_PCR_amp)  

# Checks if the chrom number is as expected
d2$Intended_chr <- d2$CHROMOSOME == d2$Chr_GRCh38_pred

# Intended_amplicon has expected chr and intended interval
# NOTE: consider renaming INtended_interval to Expected_isPCR_length
d2$Intended_amplicon <- d2$Intended_chr & d2$Intended_interval

#2086 - total number of amplicons
#nrow(d2) 

# 2040 -  amplicons meeting Intended_amplicon criteria
#sum(na.omit(d2$Intended_amplicon))

# 1997 - number of unique IDs
# length(unique(d2$UNIQID))   

# Conclusion: some amplicons have multiple isPCR products and match the Intended_amplicon criteria 

#Manual inspection
# 42 spurious amplicons
#dim(dplyr::filter(d2,  Intended_amplicon == FALSE))

# 42 PCR byproducts 
PCR_byproducts <- dplyr::filter(d2,  Intended_amplicon == FALSE) 

#unique(PCR_byproducts$UNIQID)  # 18 UNIQID in PCR_byproducts


# For activity modeling I'll focus only on these with Perfect_In_silico_specificity
isPCR <-  dplyr::filter(d2, Perfect_In_silico_specificity == TRUE)

# There are 1958 of these amplicons. This is a good set I'll be using for lm modeling
#nrow(isPCR)

2. Assay reproducibility

  • Amplicons were realigned to GRCh38 and processed by KC.
  • Read duplicates were removed prior to counting reads/amplicon.
  • GRCh37 was used for primer design and my initial isPCR.
  • Amplicon counts were generated for all ampicons ever made/used in the lab, which allows us to track potential contamination.
  • The V4 and V5 datasets refer to two biological replicates/ injections performed by JL. LSF determined good reproducibility between these two biological replicates in her initial analysis.
#Let's load the V4 and V5 data. This code has been adapted from Linda Su-Feher analysis in 20200116_Combined_Analysis.R

## load v4 + v5 data
## dims: 2576 x 19-22

data.v4 <- read.table("Amplicon_Counts/ASD1KV4_Combined_Amplicons_DupRem.txt", sep = "\t", header = T, quote = "")

data.v5 <- read.table("Amplicon_Counts/ASD1KV5_Combined_Amplicons_DupRem.txt", sep = "\t", header = T, quote = "")
# The two bed files contain dictionaries between GRCh37 and GRCh38 SNV coordinates. I think this was done using Bioconductor liftOver

# Combined_408_ASD30-1000_GRCh38_FORALLELES.bed contains the exact amplicon coordinates for STAR408, ASD30 and ASD100 libraries; and the 1700 bp guesstimates for the ASD1K library, extended 850 bp each direction from SNV coordinates.

# Combined_408_ASD30-1000_GRCh38_unflanked.bed contains SNV coordinates for ASD1K

# This is not necessary because GRCh37 coordinates were later estimated from isPCR (in_silico_expected_chr_GRCh37)

d <- read.table("Coordinates/Combined_408_ASD30-1000_GRCh38_FORALLELES.bed")
e <- read.table("Coordinates/Combined_408_ASD30-1000_GRCh38_unflanked.bed")

# Examples of amplicon ID formats:
# STAR408: "1_CACNA1C or 1UN_CACNA1C", "41_Epilepsy",  UN stands for unique or non-overlapping 
# ASD70: "27_12303.p1_Amp", all have _Amp
# ASD30: "1-13111.p1"  number - patient number p1 or s1
# ASD1k: "1340_3_56717417_A_T_13043.s1"

# A function annotating amplicons by library type
lib_annotation <- function(x){  

x$library <- ifelse(
grepl("^\\d+(UN)?_[A-Za-z0-9]+$", x$V4, perl=TRUE), "STAR408", "")

x$library <- ifelse(
grepl("_Control_", x$V4, perl=TRUE), "STAR408", x$library)

x$library <- ifelse(
grepl("^.*_Amp$", x$V4, perl = TRUE), "ASD70", x$library)

x$library <- ifelse(
grepl("^\\d+-\\d+.[sp]1$", x$V4, perl = TRUE), "ASD30", x$library)

x$library <- ifelse(
grepl("^\\d+_\\d+_\\d+_([ATCG]*)_([ATCG]*)_\\d+.(\\w)\\d$", x$V4, perl = TRUE), "ASD1k", x$library)

colnames(x) <- c("Chr", "START_GRCh38", "END_GRCh38", "UNIQID", "Library")

x
}

#dim(d)
#dim(e) # e has 20 more rows

d <- lib_annotation(d)
e <- lib_annotation(e)

# These are the two missing ASD1k amplicons in d: 
# "931_1_144520557_G_A_14547.p1"
# "2573_9_69219445_T_C_11218.p1"
#setdiff(e$UNIQID, d$UNIQID) 
#setdiff(d$UNIQID, e$UNIQID)

#length(e$UNIQID) #2596
#length(d$UNIQID) #2576

#length(unique(e$UNIQID)) # 2578 There are some duplicated UNIQIDs in e
#length(unique(d$UNIQID)) # 2576

#nrow(filter(e, Library == "ASD1k")) # 1996
#nrow(filter(d, Library == "ASD1k")) # 1994

# Reading in silico PCR GRCh37 from pre-defined chromosomes data - DEPRICATED, delete
# Pre defined chromosomes refers to running isPCR only against a the expected chromosome. 
#in_silico_expected_chr_GRCh37 <- read.csv("G:/Shared drives/Nord Lab - Data/SFRI00/ASD1000/ASD1KV5/Karol/In_silico_PCR/Pre_defined_chrom_in_silico_PCR_GRCh37/Amplicons_w_edge_coordinates_and_seq_pre_def_chrom_GRCh37_1_to_1997.csv")


# There are 1997 unique IDs, but even this chrom pre-determined algorithm found some off target products
#length(unique(df_in_silico$UNIQID))  # 1997
#length((df_in_silico$UNIQID))        # 2086
#
# one missing in the e object
#setdiff(unique(df_in_silico$UNIQID), filter(e, Library == "ASD1k")$UNIQID) #"1576_19_40388570_G_A_14009.s1" is missing
#filter(df_in_silico, UNIQID == "1576_19_40388570_G_A_14009.s1") # This amplicon is predicted to have a product.
# Below is Linda's code from 20200116_Combined_Analysis.R
# It indicates good reproducibility between the V4 and V5 replicates. 
# I modified it adding a filtering step for low represented amplicons with less than 100 counts across DNA samples

## merge data: 2576 x 40
data.merge <- merge(data.v5, data.v4, by = "Amplicon")
data.merge$Undetermined_S0.x <- NULL
data.merge$Undetermined_S0.y <- NULL

# NOTE - add filtering column instead of hard thresholding - consider at later stage in the analysis
# Filtering out amplicons with less than 100 reads at DNA level 
# data.merge <- data.merge[rowSums(data.merge[,grepl("AKg",colnames(data.merge))]) > 100,] # down to 1197 
# Removing all non ASD1k amplicons which may increase the consistency between the V4 and V5 replicates.


## Add some colors to check for contamination 
data.merge <- merge(data.merge, d[,c("UNIQID", "Library")], by.x = "Amplicon", by.y = "UNIQID")
data.merge_ASD1k <- dplyr::filter(data.merge, Library == "ASD1k")

data.merge$Library <- NULL
data.merge_ASD1k$Library <- NULL

# Calculate proportion
amp.prop <- data.merge_ASD1k
rownames(amp.prop) <- amp.prop$Amplicon
amp.prop$Amplicon <- NULL
samples <- colnames(amp.prop)

# Proportion function here: (x+1)/(sum(x)+1)  # +1 is padding
## KC: again, the coding practive below is really bad.
amp.prop <- as.data.frame(apply(amp.prop, 2, function(x) { (x+1)/(sum(x, na.rm = T)+1) }))
amp.prop$Amplicon <- rownames(amp.prop)
amp.prop.plot <- amp.prop
#dim(amp.prop.plot)

2.1 Proportion correlations - 1994 amplicons

2.1.1 DNA linear

# GGpairs diagnostic plots
setwd(wd)

# Proportiona DNA
p1 <- ggpairs(amp.prop.plot[, grepl("Kg|Kv", colnames(amp.prop.plot))], 
      lower = list(continuous = wrap("points", size = 0.5, alpha = 0.2, pch = 16), 
                   combo = "box", discrete = "blank", na = "na"),
      diag = list(discrete = "blankDiag", continuous = wrap("densityDiag", alpha = 0.4), na = "blankDiag"), 
      upper = list(continuous = wrap('cor', size = 3)))+ 
      theme_bw()+ 
      theme(panel.grid.major = element_blank(), 
            panel.grid.minor = element_blank(), 
            text = element_text(size = 8), 
            axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

p1

2.1.2 DNA log2

# Proportion log2 DNA
p2 <- ggpairs(log2(amp.prop.plot[, grepl("Kg|Kv", colnames(amp.prop.plot))]), 
      lower = list(continuous = wrap("points", size = 0.5, alpha = 0.2, pch = 16), 
                   combo = "box", discrete = "blank", na = "na"),
      diag = list(discrete = "blankDiag", continuous = wrap("densityDiag", alpha = 0.4), na = "blankDiag"), 
      upper = list(continuous = wrap('cor', size = 3)))+ 
      theme_bw()+ 
      theme(panel.grid.major = element_blank(), 
            panel.grid.minor = element_blank(), 
            text = element_text(size = 8), 
            axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

p2

2.1.3 RNA linear

# GGpairs diagnostic plots
setwd(wd)

# Proportiona DNA
p3 <- ggpairs(amp.prop.plot[, grepl("Ko|Ks", colnames(amp.prop.plot))], 
      lower = list(continuous = wrap("points", size = 0.5, alpha = 0.2, pch = 16), 
                   combo = "box", discrete = "blank", na = "na"),
      diag = list(discrete = "blankDiag", continuous = wrap("densityDiag", alpha = 0.4), na = "blankDiag"), 
      upper = list(continuous = wrap('cor', size = 3)))+ 
      theme_bw()+ 
      theme(panel.grid.major = element_blank(), 
            panel.grid.minor = element_blank(), 
            text = element_text(size = 8), 
            axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

p3

2.1.4 RNA log2

# GGpairs diagnostic plots
setwd(wd)

# Proportiona DNA
p4 <- ggpairs(log2(amp.prop.plot[, grepl("Ko|Ks", colnames(amp.prop.plot))]), 
      lower = list(continuous = wrap("points", size = 0.5, alpha = 0.2, pch = 16), 
                   combo = "box", discrete = "blank", na = "na"),
      diag = list(discrete = "blankDiag", continuous = wrap("densityDiag", alpha = 0.4), na = "blankDiag"), 
      upper = list(continuous = wrap('cor', size = 3)))+ 
      theme_bw()+ 
      theme(panel.grid.major = element_blank(), 
            panel.grid.minor = element_blank(), 
            text = element_text(size = 8), 
            axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

p4

3. Ratiometric activity

## Manually calculate ratiometric activity (RNA/DNA)
amp.act <- data.frame("amp_id" = rownames(amp.prop.plot), "Color" = "ASD1k",
                      "Ratio.V5.S.54" = 0, "Ratio.V5.S.55" = 0, "Ratio.V5.S.55b" = 0, "Ratio.V5.S.55c" = 0, 
                      "Ratio.V5.S.56" = 0, "Ratio.V5.S.56b" = 0, "Ratio.V5.S.56c" = 0, "Ratio.V5.S.57" = 0, 
                      "Ratio.V5.S.69" = 0, "Ratio.V5.S.70" = 0, "Ratio.V5.S.71" = 0, "Ratio.V5.S.71b" = 0, "Ratio.V5.S.71c" = 0, 
                      "Ratio.V4.O.49" = 0, "Ratio.V4.O.50" = 0, "Ratio.V4.O.51" = 0, "Ratio.V4.O.52" = 0, "Ratio.V4.O.53" = 0, 
                      "Ratio.V4.S.49" = 0, "Ratio.V4.S.50" = 0, "Ratio.V4.S.51" = 0, "Ratio.V4.S.52" = 0, "Ratio.V4.S.53" = 0
                      )

## manually define ratiometric activity for V5
amp.act$Ratio.V5.S.54 <- (amp.prop.plot$AKs54_S90) / (amp.prop.plot$AKg54_S83)
amp.act$Ratio.V5.S.55 <- (amp.prop.plot$AKs55_S91) / (amp.prop.plot$AKg55_S84)
amp.act$Ratio.V5.S.55b <- (amp.prop.plot$AKs55b_S97) / (amp.prop.plot$AKg55_S84)
amp.act$Ratio.V5.S.55c <- (amp.prop.plot$AKs55c_S100) / (amp.prop.plot$AKg55_S84)
amp.act$Ratio.V5.S.56 <- (amp.prop.plot$AKs56_S92) / (amp.prop.plot$AKg56_S85)
amp.act$Ratio.V5.S.56b <- (amp.prop.plot$AKs56b_S98) / (amp.prop.plot$AKg56_S85)
amp.act$Ratio.V5.S.56c <- (amp.prop.plot$AKs56c_S101) / (amp.prop.plot$AKg56_S85)
amp.act$Ratio.V5.S.57 <- (amp.prop.plot$AKs57_S93) / (amp.prop.plot$AKg57_S86)
amp.act$Ratio.V5.S.69 <- (amp.prop.plot$AKs69_S94) / (amp.prop.plot$AKg69_S87)
amp.act$Ratio.V5.S.70 <- (amp.prop.plot$AKs70_S95) / (amp.prop.plot$AKg70_S88)
amp.act$Ratio.V5.S.71 <- (amp.prop.plot$AKs71_S96) / (amp.prop.plot$AKg71_S89)
amp.act$Ratio.V5.S.71b <- (amp.prop.plot$AKs71b_S99) / (amp.prop.plot$AKg71_S89)
amp.act$Ratio.V5.S.71c <- (amp.prop.plot$AKs71c_S102) / (amp.prop.plot$AKg71_S89)

## manually define ratiometric activity for V4
amp.act$Ratio.V4.O.49 <- (amp.prop.plot$AKo49_S50) / (amp.prop.plot$AKg49_S40)
amp.act$Ratio.V4.O.50 <- (amp.prop.plot$AKo50_S51) / (amp.prop.plot$AKg50_S41)
amp.act$Ratio.V4.O.51 <- (amp.prop.plot$AKo51_S52) / (amp.prop.plot$AKg51_S42)
amp.act$Ratio.V4.O.52 <- (amp.prop.plot$AKo52_S53) / (amp.prop.plot$AKg52_S43)
amp.act$Ratio.V4.O.53 <- (amp.prop.plot$AKo53_S54) / (amp.prop.plot$AKg53_S44)
amp.act$Ratio.V4.S.49 <- (amp.prop.plot$AKs49_S45) / (amp.prop.plot$AKg49_S40)
amp.act$Ratio.V4.S.50 <- (amp.prop.plot$AKs50_S46) / (amp.prop.plot$AKg50_S41)
amp.act$Ratio.V4.S.51 <- (amp.prop.plot$AKs51_S47) / (amp.prop.plot$AKg51_S42)
amp.act$Ratio.V4.S.52 <- (amp.prop.plot$AKs52_S48) / (amp.prop.plot$AKg52_S43)
amp.act$Ratio.V4.S.53 <- (amp.prop.plot$AKs53_S49) / (amp.prop.plot$AKg53_S44)

3.1 Correlation plots - 1994 amplicons

3.1.1 log2

# Activity sample corelation plots 
## Linear
p <- ggpairs(log2(amp.act[, grepl("Ratio", colnames(amp.act))]), 
             lower = list(continuous = wrap("points", size = 0.5, alpha = 0.2, pch = 1), 
                          combo = "box", discrete = "blank", na = "na"),
             diag = list(discrete = "blankDiag", continuous = wrap("densityDiag", alpha = 0.4), 
                         na = "blankDiag"), 
             upper = list(continuous = wrap('cor', size = 3))) + 
             theme_bw()+ 
             theme(panel.grid.major = element_blank(), 
                   panel.grid.minor = element_blank(), 
                   text = element_text(size = 10), 
                   axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

p

4. Mean ratiometric activity and proportions

#Activity Mean and SD
library(genefilter)
library(reshape2)
library(data.table)

amp.act_summary <- amp.act[,1:2]

amp.act_summary$Mean_V5 <- rowMeans(amp.act[,c(3:15)])
amp.act_summary$Mean_V4 <- rowMeans(amp.act[,c(16:25)])
amp.act_summary$Mean_V4.O <- rowMeans(amp.act[,c(16:20)])
amp.act_summary$Mean_V4.S <- rowMeans(amp.act[,c(21:20)])
amp.act_summary$Mean_All <- rowMeans(amp.act[,c(3:25)])

amp.act_summary$SD_V5 <- rowSds(as.matrix(amp.act[,c(3:15)]))
amp.act_summary$SD_V4 <- rowSds(as.matrix(amp.act[,c(16:25)]))
amp.act_summary$SD_V4.O <- rowSds(as.matrix(amp.act[,c(16:20)]))
amp.act_summary$SD_V4.S <- rowSds(as.matrix(amp.act[,c(21:20)]))
amp.act_summary$SD_All <- rowSds(as.matrix(amp.act[,c(3:25)]))



# Proportions
#
#colnames(amp.prop.plot)
#colnames(data.v4)
#colnames(data.v5)

#Let's construct a proper metadata df
metadata <- data.frame("Sample_name" = c(colnames(data.v4)[2:18], colnames(data.v5)[2:21]))
metadata$Replicate <- ifelse(metadata$Sample_name %in% colnames(data.v4)[2:18], "V4", "V5")

class_of_nucleic_acid <- ifelse(grepl("AKg", metadata$Sample_name), "gDNA", NA)
class_of_nucleic_acid <- ifelse(grepl("AKs|AKo", metadata$Sample_name), "cDNA", class_of_nucleic_acid)
class_of_nucleic_acid <- ifelse(grepl("AKv", metadata$Sample_name), "virus", class_of_nucleic_acid)

metadata$Nucleic_acid <- class_of_nucleic_acid

V4_gDNA_samples <- dplyr::filter(metadata, Replicate == "V4", Nucleic_acid == "gDNA")$Sample_name
V4_cDNA_samples <- dplyr::filter(metadata, Replicate == "V4", Nucleic_acid == "cDNA")$Sample_name
V5_gDNA_samples <- dplyr::filter(metadata, Replicate == "V5", Nucleic_acid == "gDNA")$Sample_name
V5_cDNA_samples <- dplyr::filter(metadata, Replicate == "V5", Nucleic_acid == "cDNA")$Sample_name

gDNA_samples <- dplyr::filter(metadata, Nucleic_acid == "gDNA")$Sample_name
cDNA_samples <- dplyr::filter(metadata, Nucleic_acid == "cDNA")$Sample_name


amp.prop.plot$Color <- "ASD1k"

amp.prop_summary <- amp.prop.plot[,c("Amplicon", "Color")]
rownames(amp.prop_summary) <- NULL

#head(amp.prop_summary)


amp.prop_summary$Mean_gDNA <- rowMeans(amp.prop.plot[,gDNA_samples])
amp.prop_summary$Mean_cDNA <- rowMeans(amp.prop.plot[,cDNA_samples])
amp.prop_summary$Mean_gDNA_V4 <- rowMeans(amp.prop.plot[,V4_gDNA_samples])
amp.prop_summary$Mean_cDNA_V4 <- rowMeans(amp.prop.plot[,V4_cDNA_samples])
amp.prop_summary$Mean_gDNA_V5 <- rowMeans(amp.prop.plot[,V5_gDNA_samples])
amp.prop_summary$Mean_cDNA_V5 <- rowMeans(amp.prop.plot[,V5_cDNA_samples])

amp.prop_summary$SD_gDNA <- rowSds(amp.prop.plot[,gDNA_samples])
amp.prop_summary$SD_cDNA <- as.numeric(rowSds(amp.prop.plot[,cDNA_samples]))
amp.prop_summary$SD_gDNA_V4 <- as.numeric(rowSds(amp.prop.plot[,V4_gDNA_samples]))
amp.prop_summary$SD_cDNA_V4 <- as.numeric(rowSds(amp.prop.plot[,V4_cDNA_samples]))
amp.prop_summary$SD_gDNA_V5 <- as.numeric(rowSds(amp.prop.plot[,V5_gDNA_samples]))
amp.prop_summary$SD_cDNA_V5 <- as.numeric(rowSds(amp.prop.plot[,V5_cDNA_samples]))


# Applying a threshold on proportions
amp.prop_summary$Pass_prop_gDNA_and_cDNA <- ifelse(amp.prop_summary$Mean_gDNA > 2^-15 & 
                                                     amp.prop_summary$Mean_cDNA > 2^-15, TRUE, FALSE)


## Add Boolean columns denoting min count tests
 min_count = 100
 min_count_gDNA <- rowSums(data.merge[,gDNA_samples]>min_count) >= ncol(data.merge[,gDNA_samples])
 min_count_gDNA_V4 <- rowSums(data.merge[,V4_gDNA_samples]>min_count) >= ncol(data.merge[,V4_gDNA_samples])
 min_count_gDNA_V5 <- rowSums(data.merge[,V5_gDNA_samples]>min_count) >= ncol(data.merge[,V5_gDNA_samples])
 
 min_count_cDNA <- rowSums(data.merge[,cDNA_samples]>min_count) >= ncol(data.merge[,cDNA_samples])
 min_count_cDNA_V4 <- rowSums(data.merge[,V4_cDNA_samples]>min_count) >= ncol(data.merge[,V4_cDNA_samples])
 min_count_cDNA_V5 <- rowSums(data.merge[,V5_cDNA_samples]>min_count) >= ncol(data.merge[,V5_cDNA_samples])
 
 count_Booleans <- data.frame(
   "Amplicon" = data.merge$Amplicon,
   "min_count_gDNA" = min_count_gDNA,
   "min_count_gDNA_V4" = min_count_gDNA_V4,
   "min_count_gDNA_V5" =  min_count_gDNA_V5,
   
   "min_count_cDNA" = min_count_cDNA,
   "min_count_cDNA_V4" = min_count_cDNA_V4,
   "min_count_cDNA_V5" =  min_count_cDNA_V5
 )

 
# Building a comprehensive data frame
 y <- merge(amp.prop_summary, count_Booleans, by = "Amplicon") # 1994 rows, Only ASD1k amplicons
 
 y_ASD1k <- dplyr::filter(y, Color == "ASD1k") # 1994 rows
library(ggplot2)
library(genefilter)
library(ggpmisc)

# A rectangle delinating DNA and RNA proportion thresholds < 2^-15
d=data.frame(x1=c(-Inf, -Inf), x2=c(+Inf, -15), y1=c(-Inf,-15), y2=c(-15, +Inf))


# Passing min gDNA counts on all samples
p1 <- ggplot()+
  geom_point(data = y_ASD1k, aes(x = log2(Mean_gDNA), 
                                 y = log2(Mean_cDNA), 
                                 color = min_count_gDNA), alpha = 0.3)+
  theme_bw()+
  geom_rect(data=d, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), color=NA, alpha=0.3)+
  labs(title = "Amplicons with > 100 counts in all DNA")+
  theme(plot.title = element_text(hjust = 0.5),
        legend.position="bottom")


p2 <- ggplot()+
  geom_point(data = y_ASD1k, aes(x = log2(Mean_gDNA), y = log2(Mean_cDNA), 
                                 color = min_count_gDNA_V4), alpha = 0.3)+
  theme_bw()+
  geom_rect(data=d, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), color=NA, alpha=0.3)+
  labs(title = "Amplicons with > 100 counts in V4 replicate DNA")+
  theme(plot.title = element_text(hjust = 0.5),
        legend.position="bottom")



p3 <- ggplot()+
  geom_point(data = y_ASD1k, aes(x = log2(Mean_gDNA), y = log2(Mean_cDNA), 
                                 color = min_count_gDNA_V5), alpha = 0.3)+
  theme_bw()+
  geom_rect(data=d, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), color=NA, alpha=0.3)+
  labs(title = "Amplicons with > 100 counts in V5 replicate DNA")+
  theme(plot.title = element_text(hjust = 0.5),
        legend.position="bottom")





# Passing min gDNA counts on all samples
y$min_count_gDNA_and_cDNA <- y$min_count_cDNA & y$min_count_gDNA
y_ASD1k$min_count_gDNA_and_cDNA <- y_ASD1k$min_count_cDNA & y_ASD1k$min_count_gDNA

p4 <- ggplot()+
  geom_point(data = y_ASD1k, aes(x = log2(Mean_gDNA), y = log2(Mean_cDNA), 
                                 color = min_count_gDNA_and_cDNA), alpha = 0.3)+
  theme_bw()+
  geom_rect(data=d, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), color=NA, alpha=0.3)+
  labs(title = "Amplicons with > 100 counts in DNA and RNA samples")+
  theme(plot.title = element_text(hjust = 0.5),
        legend.position="bottom")


plot_grid(p1, p2, p3, p4, 
          labels = c("A", "B", "C", "D"), 
          rel_widths = c(1.2, 1.2),
          vjust = 0.9)

### Dataset for lm ###
# Filtering criteria:
#  1. > 100 reads across all gDNA samples, and > 100 across all cDNA samples
#  2. Mean gDNA proportions and mean cDNA proportions > 2^-15

y$lm_dataset <- y$min_count_gDNA_and_cDNA & y$Pass_prop_gDNA_and_cDNA
y_ASD1k$lm_dataset <- y_ASD1k$min_count_gDNA_and_cDNA & y_ASD1k$Pass_prop_gDNA_and_cDNA

p1 <- ggplot()+
  geom_point(data = y_ASD1k, aes(x = log2(Mean_gDNA), y = log2(Mean_cDNA), color = lm_dataset), alpha = 0.3)+
  theme_bw()+
  geom_rect(data=d, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), color=NA, alpha=0.3)+
  labs(title = "Dataset cleaning for building the lm model")+
  theme(plot.title = element_text(hjust = 0.5),
        legend.position="bottom")



## plotting lm dataset ##
y_ASD1k_lm <- dplyr::filter(y_ASD1k, lm_dataset == TRUE)

formula <- y ~ x

p2 <- ggplot(y_ASD1k_lm, aes(x = log2(Mean_gDNA), y = log2(Mean_cDNA)))+
  geom_point(alpha = 0.3)+
  theme_bw()+
  geom_abline(intercept = 0, color = "red", linetype = 2)+
  geom_smooth(formula = formula, method = 'lm', color = "black")+
  stat_poly_eq(formula = formula, 
                aes(label = paste(..rr.label.., ..eq.label.., sep = "~~~")), 
                parse = TRUE)+
 labs(title = "Simple lm fit to the cleaned data, n = 748 amplicons")+
 theme(plot.title = element_text(hjust = 0.5),
        legend.position="bottom")


plot_grid(p1, p2,
          labels = c("A", "B"), 
          rel_widths = c(1.2, 1.2),
          vjust = 0.9)

# save.image("G:/Shared drives/Nord Lab - Data/SFRI00/ASD1000/ASD1KV5/Karol/ASD1k_lm_10_07_2020.RData")
# load("G:/Shared drives/Nord Lab - Data/SFRI00/ASD1000/ASD1KV5/Karol/ASD1k_lm_10_07_2020.RData")
# setwd("G:/Shared drives/Nord Lab - Data/SFRI00/ASD1000/ASD1KV5/Karol/")

5. GC content

# I'm using GC values from in silico PCR prediction, limiting the subset of amplicons to those with only 1 predicted product/
#filter(d2, Perfect_In_silico_specificity == TRUE)

# Merging GC dataset with perfect in silico specificity reduced amplicon number from 748 to 730. Not much loss!
y_ASD1k_lm_GC <- merge(y_ASD1k_lm, dplyr::filter(d2, Perfect_In_silico_specificity == TRUE)[,c(1, 17)], by.x = "Amplicon", by.y = "UNIQID")

5.1 Plotting and modeling data with GC content

formula <- y ~ x

y_ASD1k_lm_GC$GC_ab_mean <- ifelse(y_ASD1k_lm_GC$GC_content > mean(na.omit(y_ASD1k_lm_GC$GC_content)), TRUE, FALSE)

#y_ASD1k_lm_GC[-c(252, 499, 576),] # Removes rows containing GC values
# na.omit(y_ASD1k_lm_GC)  - the same, just simpler

ggplot(na.omit(y_ASD1k_lm_GC), 
       aes(x = log2(Mean_gDNA), y = log2(Mean_cDNA), 
           color = GC_content))+
  geom_point(alpha = 0.5)+
  theme_bw()+
  geom_abline(intercept = 0, color = "red", linetype = 2)+
  geom_smooth(formula = formula, method = 'lm', color = "black")+
  stat_poly_eq(formula = formula, 
                aes(label = paste(..rr.label.., ..eq.label.., sep = "~~~")), 
                parse = TRUE)+
  scale_color_gradient2(midpoint = mean(na.omit(y_ASD1k_lm_GC$GC_content)), low="blue3", mid="white",
                     high="red3", space ="Lab" )+
  facet_wrap(~ GC_ab_mean)+
  labs(title = "DNA vs RNA proportions, split at mean GC content")+
  theme(plot.title = element_text(hjust = 0.5),
        legend.position="bottom")
There seems to be limited if any correlation of GC content and amplicon activity

There seems to be limited if any correlation of GC content and amplicon activity